Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
复制标题
脑电图深度学习：近十年关键期的进展
DOI：
10.1109/tcds.2021.3079712
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发表时间：
2022-06-01
影响因子：
5
通讯作者：
Li, Junhua
中科院分区：
文献类型：
--
作者：
Gong, Shu;Xing, Kaibo;Li, Junhua
深度学习在广泛的领域取得了优异的性能，特别是在语音识别和计算机视觉方面。脑电图（EEG）的研究相对较少，但在过去十年中仍取得了重大进展。由于缺乏对EEG深度学习的全面和广泛覆盖的调查，我们试图总结最近的进展，以提供概述，以及未来发展的前景。我们首先简要介绍了EEG信号的伪影去除，然后介绍了已用于EEG处理和分类的深度学习模型。随后，通过将其归类为脑机接口，疾病检测和情感识别等组，回顾了深度学习在EEG中的应用。随后进行了讨论，其中提出了深度学习的优点和缺点，并提出了EEG深度学习的未来方向和挑战。希望本文能作为对过去脑电深度学习研究的总结，也是基于深度学习的脑电研究进一步发展和取得成果的开端。
Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for electroencephalogram (EEG), but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups, such as brain–computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this article could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.